Advertisement clicking prediction by using multiple criteria mathematical programming

Lee, Jongwon; Shi, Yong; Wang, Fang; Lee, Heeseok; Kim, Heung Kee

doi:10.1007/s11280-015-0353-1

Cited by 9 publications

(3 citation statements)

References 23 publications

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“…For example, Tejeda-Lorente et al (2014) resorted to switching between a collaborative and a content-based approaches, whereas other researches combine both techniques within the same system (Kardan and Ebrahimi, 1974). Other approaches may include different hybridisation techniques in order to improve their recommendations by taking into account more behavioural patterns and criteria (Chen et al, 2014;Lee et al, 2016).…”

Section: Recommendations Inference Methodologiesmentioning

confidence: 99%

An adaptive and interactive recommendation model for consumers' behaviours prediction

Haddad

Baazaoui

2018

IJDMMM

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Recommendation algorithms aim at predicting customers' interests and purchases using different ideas and hypotheses. Consequently, system designers need to choose the recommendation approach that is the most suitable with regard to their products' nature and consumers' behaviours within the application field. In this paper, we propose an adaptive recommendation model based on statistical modelling to assist consumers facing choice overload by predicting their interests and consumption behaviours. We also propose a dynamic variant of the model taking into account the recommendations' time-value during interactive online recommendation scenarios. Our proposal has endured a two-fold evaluation. First, we conducted an offline comparative study on the MovieLens recommendation dataset in order to assess our model's performance with regard to several widely adopted recommendation techniques. Then, the model was evaluated within a real time online news recommendation platform to highlight its adaptability, scalability and efficiency in a highly interactive application domain.

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Section: Recommendations Inference Methodologiesmentioning

confidence: 99%

An adaptive and interactive recommendation model for consumers' behaviours prediction

Haddad

Baazaoui

2018

IJDMMM

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“…The author described advertisement-clicking problem [82] via 04 multiple criteria mathematical programming facsimiles.Click-through-rate (CTR) forecast used multi-criteria-linear-regression (MCLR) and kernel-based-multiplecriteria-regression (KMCR) processes, whereas for predicting the ads multi-criteria-linear-programming (MCLP) and kernel-based-multiple-criteria-programming (KMCP) processes are used. The extensive experiments shows that the MCLP and KMCP prototypes have superior performance immovability and can be cast-off to efficiently grip over interactive steering application for online advertisement glitches.…”

Section: Missingmentioning

confidence: 99%

Survey on Recommender System Using Deep Learning Networks

Jaiswal

2020

Artificial Intelligence Evolution

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In today’s times, the recommended system is a very powerful weapon of shoppers that is very helpful in advancing the Internet, personalized tendencies, and online shopping. The recommended system is used primarily for commercial benefit. The recommended system works on the strength of the user’s past shopping experience and its feedback, whether it is positive or negative. Hence the recommended system is also an innovative method. There is a deferred method of the recommended system which has its own advantages and disadvantages. In this paper, the Recommender system based on deep learning is proposed, and also discussed the challenges and issues which are related to the deep learning based recommender system. i.e., Accuracy, Cold Start Problem, Scalability States etc. In this paper, we have also discussed the work done so far, which has been given by various scientists / researchers / investigators. Advancement of machine learning and deep learning is very big, in today’s era. This study will help the Researcher to move forward.

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“…In addition to this project, online advertisement problems can be captured as advertisement clicking predictions, which can further be viewed as click-through rate (CTR) prediction and identifying clicked ads in a set of ads. Some well-known data mining methods, such as SVM and multiple criteria mathematical programming can be effectively applied in handling these online ad big data problems (Lee, Shi, Wang, Lee, & Kim, 2015). 6 CNY in service fees each day.…”

Section: Online Advertisement With Big Datamentioning

confidence: 99%

Challenges to Engineering Management in the Big Data Era

Shi¹

2015

Front. Eng

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This paper presents a review of the challenges to engineering management in the Big Data Era as well as the Big Data applications. First, it outlines the definitions of big data, data science and intelligent knowledge and the history of big data. Second, the paper reviews the academic activities about big data in China. Then, it elaborates a number of challenging big data problems, including transforming semi-structured and non-structured data into "structured format" and explores the relationship of data heterogeneity, knowledge heterogeneity and decision heterogeneity. Furthermore, the paper reports various real-life applications of big data, such as financial and petroleum engineering and internet business.

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Advertisement clicking prediction by using multiple criteria mathematical programming

Cited by 9 publications

References 23 publications

An adaptive and interactive recommendation model for consumers' behaviours prediction

An adaptive and interactive recommendation model for consumers' behaviours prediction

Survey on Recommender System Using Deep Learning Networks

Challenges to Engineering Management in the Big Data Era

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